data = data.reshape((n * data.shape[1], n * data.shape[3]) +
data.shape[4:])
plt.imshow(data)
plt.axis('off')
plt.show()
if __name__ == '__main__':
caffe.set_device(0)
caffe.set_mode_gpu()
net_file = '/home/shenyaxin/color/deploy.prototxt'
caffe_model = '/core1/data/home/liuhuawei/tools/models/bvlc_reference_caffenet.caffemodel'
net = caffe.Net(net_file, caffe_model, caffe.TEST)
mysql = MysqlOperator()
#shop_mete = get_data(mysql, 0)
datas = get_data(mysql, 1)
src_id_list = []
fea_list = []
batch_size = 32
bs = 32
for i in range(0, len(datas), batch_size):
if (i + batch_size) > len(datas):
bs = len(datas) - i
data_batch = datas[i:i + bs]
imgs = []
hsvs = []
hists = []
itemids = []
for data in data_batch:
def deconv_each():
caffe.set_device(1)
caffe.set_mode_gpu()
net_file = '/home/shenyaxin/color/deploy.prototxt'
denet_file = '/home/shenyaxin/color/each_filter.prototxt'
caffe_model = '/core1/data/home/liuhuawei/tools/models/bvlc_reference_caffenet.caffemodel'
net = caffe.Net(net_file, caffe_model, caffe.TEST)
denet = caffe.Net(denet_file, caffe.TEST)
mysql = MysqlOperator()
#shop_mete = get_data(mysql, 0)
datas = get_data(mysql, 1)
src_id_list = []
fea_list = []
batch_size = 1
bs = 1
for i in range(0, len(datas), batch_size):
if (i + batch_size) > len(datas):
bs = len(datas) - i
data_batch = datas[i:i + bs]
imgs = []
itemids = []
for data in data_batch:
itemid = data['src_id']
itemids.append(itemid)
path = data['url']
xmin = int(data['xmin'])
ymin = int(data['ymin'])
width = int(data['width'])
height = int(data['height'])
bbox = [xmin, ymin, xmin + width, ymin + height]
img = transform_img(path, bbox)
imgs.append(img)
#transformerd = caffe.io.Transformer({'test_photod': net.blobs['data'].data.shape})
#transformerd.set_transpose('test_photod', (2, 0, 1))
#transformerd.set_mean('test_photod', np.array([104,117,123]))
imgs = np.array(imgs)
net.blobs["data"].reshape(bs, 3, 224, 224)
net.blobs["data"].data[...] = imgs
output = net.forward()
all_de = [] #print net.params['conv1'][0].shape
for step in range(0, 96):
denet.params['conv1'][0].data[
...] = net.params['conv1'][0].data[step].reshape(
denet.params['conv1'][0].data.shape)
denet.params['conv1t'][0].data[
...] = net.params['conv1'][0].data[step].reshape(
denet.params['conv1t'][0].data.shape)
denet.blobs["data"].reshape(bs, 3, 224, 224)
denet.blobs["data"].data[...] = imgs
deout = denet.forward()
img_t = denet.blobs['conv1t'].data.copy()
#print net.params['conv1'][0].data.shape, img_t.shape
img_t = detrans(img_t)
#print imgs.shape, img_t.shape
#plt.subplot(1,2,1)
#plt.title('origin')
#plt.imshow(imgs[0].transpose((1,2,0)))
#plt.axis('off')
#plt.subplot(1,2,2)
#plt.title('deconv')
#plt.imshow(img_t[0].transpose((1,2,0)))
#plt.axis('off')
#plt.show()
#plt.savefig('1.png')
imga = imgs[0, (1, 2, 0), :, :].transpose((1, 2, 0)).astype(int)
imgb = img_t[0, (1, 2, 0), :, :].transpose((1, 2, 0)).astype(int)
all_de.append(imgb)
all_de = np.array(all_de)
vis_square(all_de, i)
#misc.imsave('img/%d1.png'%(i),imga)
#misc.imsave('img/%d2.png'%(i),imgb)
#with open('1.txt','w') as f:
# f.write(str(imgs[0]))
#with open('2.txt','w') as f2:
# f2.write(str(img_t[0]))
if i == 10:
exit()